- Course and business case study introduction
- Data Cleaning, pre-processing & data visualization
- Understanding business objective to be modelled based on the case study
WEEK 2: Classification Problem, Data Sampling, Feature Selection and Model Building
- Introduction to machine learning
- Supervised classification problem (Machine learning packages in R)
- Perform feature selection and processing on given data
- Model building based on classification techniques
WEEK 3: Evaluation Metrics, Scoring Model Output, Leaderboard Prediction
- Build machine learning model like decision trees, ensemble modelling etc.
- Evaluation metric like confusion matrix, RMSE etc.
- Leaderboard scoring for peer-2-peer feedback
WEEK 4: Visualization of Modeling Output and Analysis of Features & Model for Business Case Study
- Model building and Leaderboard updates on a daily basis visualize the output of the model
- Interpret modelling results for case study objective